SoroushVahidi/scidocs huggingface dataset:
An ubiquitous miniaturized android based ECG monitoring system
(2013)

Miniaturization of devices has brought a lot of improvements in the healthcare delivery as a result of advancenments in Integrated circuit technologies. As a result, proper and effective way of analyzing various body conditions and diseases can be achieved. When the heart muscles repolarize and depolarize, it generates electrical impulses which could help the doctor or the health professional to diagnose various heart abnormalities. The aim of this project is to design and develop a miniaturized ECG system displayed on both PC and Tablet for preliminary diagnoses of the heart. This system utilizes a programmable single-chip microcontroller for analysing the biosignals to indicate the condition of the heart. The system is incomporated with alarm systems which prompt the doctor if there is any abnormality from a patient's heart. These abnormalities could be bradycardia or tachycardia. This system is built with a provision of enabling wireless transmission of ECG signals to a PC or TABLET through BLUETOOTH and ANDROID PLATFORM. The android application can be installed and tested on any smart phone or tablet and screen resolution can be adjusted later as per requirement. This helps the doctor to have visual description of the patient ECG without the need of mounted monitors. The experimental results show that device is clinically useful, compact, cost effective and user friendly.
A probabilistic theory of clustering
(2004)

Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is di0erent with pattern classi1cation, for which two fundamental characteristics can be stated: (1) the error of a classi1er can be estimated using “test data,” and (2) a classi1er can be learned using “training data.” This paper presents a probabilistic theory of clustering, including both learning (training) and error estimation (testing). The theory is based on operators on random labeled point processes. It includes an error criterion in the context of random point sets and representation of the Bayes (optimal) cluster operator for a given random labeled point process. Training is illustrated using a nearest-neighbor approach, and trained cluster operators are compared to several classical clustering algorithms. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
A survey of Ambient Assisted Living systems: Challenges and opportunities
(2016)

As the research in Ambient Assisted Living (AAL) matures, we expect that data generated from AAL IoT devices will benefit from analysis by well established machine learning techniques. There is also potential that new research in ML and Artificial Intelligence (AI) can be used on data generated from the sensors used in AAL. In this paper we present a survey of the research in the related topics, identify its shortcomings and propose future work that will integrate these fields by collecting ambient sensor data and process the data by ML framework which can detect and classify activities.
Investigating the Implications of 3D Printing in Special Education
(2016)

Consumer-grade digital fabrication such as 3D printing is on the rise, and we believe it can be leveraged to great benefit in special education. Although 3D printing is infiltrating mainstream education, little research has explored 3D printing in the context of students with special support needs. We describe our studies on this topic and the resulting contributions. We initially conducted a formative study exploring the use of 3D printing at three locations serving populations with varying ability, including individuals with cognitive, motor, and visual impairments. We found that 3D design and printing perform three functions in special education: (1) STEM engagement, (2) creation of educational aids for accessible curriculum content, and (3) making custom adaptive devices. As part of our formative work, we also discussed a case study in the codesign of an assistive hand grip created with occupational therapists at one of our investigation sites. This work inspired further studies on the creation of adaptive devices using 3D printers. We identified the needs and constraints of these therapists and found implications for a specialized 3D modeling tool to support their use of 3D printers. We developed GripFab, 3D modeling software based on feedback from therapists, and used it to explore the feasibility of in-house 3D object designs in support of accessibility. Our contributions include case studies at three special education sites and discussion of obstacles to efficient 3D printing in this context. We have extended these contributions with a more in-depth look at the stakeholders and findings from GripFab studies. We have expanded our discussion to include suggestions for researchers in this space, in addition to refined suggestions from our earlier work for technologists creating 3D modeling and printing tools, therapists seeking to leverage 3D printers, and educators and administrators looking to implement these design tools in special education environments.
Inferring international and internal migration patterns from Twitter data
(2014)

Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.
Manipulating the soil microbiome to increase soil health and plant fertility
(2012)

A variety of soil factors are known to increase nutrient availability and plant productivity. The most influential might be the organisms comprising the soil microbial community of the rhizosphere, which is the soil surrounding the roots of plants where complex interactions occur between the roots, soil, and microorganisms. Root exudates act as substrates and signaling molecules for microbes creating a complex and interwoven relationship between plants and the microbiome. While individual microorganisms such as endophytes, symbionts, pathogens, and plant growth promoting rhizobacteria are increasingly featured in the literature, the larger community of soil microorganisms, or soil microbiome, may have more far-reaching effects. Each microorganism functions in coordination with the overall soil microbiome to influence plant health and crop productivity. Increasing evidence indicates that plants can shape the soil microbiome through the secretion of root exudates. The molecular communication fluctuates according to the plant development stage, proximity to neighboring species, management techniques, and many other factors. This review seeks to summarize the current knowledge on this topic.
Point-to-Multipoint Ultrasonic Communication Modem for Ubiquitous Underwater Wireless Sensor Networks
(2008)

Recently, researches on underwater sensor networks (USN) for ocean development and disaster prevention have been emerged as one of interesting topics. Since low-power, high-speed and inexpensive communication modem is a prerequisite for deployment of USN, we design and implement an underwater modem by utilizing general-purpose waterproof ultrasonic sensors in this paper. Also, we make an experiment in a water tank containing sensor nodes and a sink node, a gateway, and show that point-to-multipoint communication is possible at a data rate of 1 kbps.
Steatocystoma simplex in penile foreskin: a case report
(2016)

BACKGROUND
Steatocystoma simplex is an uncommon skin lesion with a histological pattern that is identical to that of steatocystoma multiplex. We are reporting this case of steatocystoma simplex for its uncommon location in the penile foreskin, and its occurrence in a Wapishana man.


CASE PRESENTATION
A 56-year-old man of Wapishana ethnicity presented with complaints of referred penile discomfort and pain during sexual intercourse for 5 years. A physical examination revealed a mobile, compressible subcutaneous non-tender mass of 4 cm diameter located on the left-side of his penile foreskin. There were no signs of inflammation, no grip on the penile shaft, and no urethral discharge or enlargement of lymph nodes. We found no evidence of other cysts on cutaneous examination. We performed classical excision of the lesion under local anesthesia and confirmed the diagnosis of steatocystoma with the pathological report. As there were no complications, we discharged him the same day.


CONCLUSION
Steatocystoma can be considered a differential diagnosis for cystic lesions on and around the penis.
Comparison of surgical variables and short-term postoperative complications in healthy dogs undergoing ovariohysterectomy or ovariectomy.
(2011)

OBJECTIVE
To determine whether ovariohysterectomy (OVH) required more time to complete and was associated with more short-term postoperative complications than ovariectomy (OVE) in dogs.


DESIGN
Randomized prospective clinical trial.


ANIMALS
40 healthy, sexually intact female dogs.


PROCEDURES
OVH (in 20 dogs) or OVE (20 dogs) was performed by use of standardized anesthetic and surgical protocols. Physical characteristics of the dogs, surgical variables, pain scores derived from behavior-based composite pain scales, and surgical wound characteristics were analyzed.


RESULTS
Body weight, age, body condition score, and distance between the sternal manubrium and the pubic rim were comparable among dogs that underwent either surgical procedure. Body weight was positively correlated with the total duration of the procedure and with time required for closure of the surgical wound. No effect of body condition score was determined for any variable. Skin and fascia incision lengths relative to the distance from the sternal manubrium to pubic rim were significantly greater in dogs that underwent OVH, compared with those of dogs that underwent OVE, but total surgical time was not different for the 2 procedures. No other significant differences were detected between the 2 groups.


CONCLUSIONS AND CLINICAL RELEVANCE
Significant differences in total surgical time, pain scores, and wound scores were not observed between dogs that underwent OVH and dogs that underwent OVE via standardized protocols.
Towards a Business Process Management Maturity Model
(2005)

Business Process Management (BPM) has been identified as the number one business priority by a recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management to name a few. Organisations increasingly recognize the requirement for an increased process orientation and require appropriate comprehensive frameworks, which help to scope and evaluate their BPM initiative. This research project aims toward the development of a holistic and widely accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper provides an overview about the current model with a focus on the actual model development utilizing a series of Delphi studies. The development process includes separate studies that focus on further defining and expanding the six core factors within the model, i.e. strategic alignment, governance, method, Information Technology, people and culture.
Guidelines for conducting and reporting case study research in software engineering
(2008)

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.
Capability maturity model, version 1.1
(1993)

The capability maturity model (CMM), developed to present sets of recommended practices in a number of key process areas that have been shown to enhance software-development and maintenance capability, is discussed. The CMM was designed to help developers select process-improvement strategies by determining their current process maturity and identifying the issues most critical to improving their software quality and process. The initial release of the CMM, version 1.0, was reviewed and used by the software community during 1991 and 1992. A workshop on CMM 1.0, held in April 1992, was attended by about 200 software professionals. The current version of the CMM is the result of the feedback from that workshop and ongoing feedback from the software community. The technical report that describes version 1.1. is summarised.<<ETX>>
Innovative Contactless Energy Transfer Accessory for Rotary Ultrasonic Machining and Its Circuit Compensation Based on Coil Turns
(2017)

An innovative concentric rotary ultrasonic machining (RUM) device based on local induction is proposed. The device is used as a machine tool accessory that can add the RUM to any machining center. Rather than using a complete ring, a portion of a ring is used as the primary core in the proposed rotary transformer, which eliminates spindle speed limits. The simplified configuration provides increased safety and is convenient for material processing applications. Additionally, a circuit compensation method based on coil turns is proposed. It takes the whole circuit, reliability of electronic components, transmission efficiency, and output power into consideration. We use this method to choose the optimal number of coil turns and compensation elements required to achieve increased and safe power transmission performance. This paper also demonstrates the performance of the device through experimentation. Finally, practicability is discussed.
What Actually Wins Soccer Matches : Prediction of the 2011-2012 Premier League for Fun and Profit
(2013)

Sports analytics is a fascinating problem area in which to apply statistical learning techniques. This thesis brings new data to bear on the problem of predicting the outcome of a soccer match. We use frequency counts of in-game events, sourced from the Manchester City Analytics program, to predict the 380 matches of the 2011-2012 Premier League season. We generate prediction models with multinomial regression and rigorously test them with betting simulations. An extensive review of prior efforts is presented, as well as a novel theoretically optimal betting strategy. We measure performance different feature sets and betting strategies. Accuracy and simulated profit far exceeding those of all earlier efforts are achieved.
Column-store support for RDF data management: not all swans are white
(2008)

This paper reports on the results of an independent evaluation of the techniques presented in the VLDB 2007 paper “Scalable Semantic Web Data Management Using Vertical Partitioning”, authored by D. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach [1]. We revisit the proposed benchmark and examine both the data and query space coverage. The benchmark is extended to cover a larger portion of the query space in a canonical way. Repeatability of the experiments is assessed using the code base obtained from the authors. Inspired by the proposed vertically-partitioned storage solution for RDF data and the performance figures using a column-store, we conduct a complementary analysis of state-of-the-art RDF storage solutions. To this end, we employ MonetDB/SQL, a fully-functional open source column-store, and a well-known – for its performance – commercial row-store DBMS. We implement two relational RDF storage solutions – triple-store and vertically-partitioned – in both systems. This allows us to expand the scope of [1] with the performance characterization along both dimensions – triple-store vs. vertically-partitioned and row-store vs. column-store – individually, before analyzing their combined effects. A detailed report of the experimental test-bed, as well as an in-depth analysis of the parameters involved, clarify the scope of the solution originally presented and position the results in a broader context by covering more systems.
Machine Learning for Intelligent Systems
(1997)

Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI’s original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to this trend and thus holds interest for the broader AI research community. I also suggest some steps to encourage further research along these lines.
Learning narrative structure from annotated folktales
(2012)

Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp’s influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, StuggleVictory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM’s large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp’s categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identified with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp’s original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural influences on cognition and understanding cultural differences as captured in stories. Dissertation Supervisor: Patrick H. Winston Professor, Electrical Engineering and Computer Science Dissertation Committee: Whitman A. Richards Professor, Brain & Cognitive Sciences Peter Szolovits Professor, Electrical Engineering and Computer Science & Harvard-MIT Division of Health Sciences and Technology Joshua B. Tenenbaum Professor, Brain & Cognitive Sciences
Neural computation and self-organizing maps - an introduction
(1992)

In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line neural computation and self organizing maps an introduction computation neural systems series book, as the world window, as many people suggest.
Comparing NoSQL MongoDB to an SQL DB
(2013)

NoSQL database solutions are becoming more and more prevalent in a world currently dominated by SQL relational databases. NoSQL databases were designed to provide database solutions for large volumes of data that is not structured. However, the advantages (or disadvantages) of using a NoSQL database for data that is structured, and not necessarily "Big," is not clear. There are not many studies that compare the performance of processing a modest amount of structured data in a NoSQL database with a traditional relational database. In this paper, we compare one of the NoSQL solutions, MongoDB, to the standard SQL relational database, SQL Server. We compare the performance, in terms of runtime, of these two databases for a modest-sized structured database. Results show that MongoDB performs equally as well or better than the relational database, except when aggregate functions are utilized.
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
(2016)

In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a firststage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence’s first-stage representation to attend words appeared in itself, which is called ”Inner-Attention” in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of ”Inner-Attention” mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.